Bayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics.
"[L]et me stress that the design and the printing of the book are both of the highest quality, numerous tree graphs appearing seamlessly at the right place [making captions superfluous], different fonts making parts more coherent and so on. I thus hope it is obvious I strongly recommend reading the book to all involved in any level of decision management! Or teaching it."
Xi'an's Og Blog
"The preface explains that the book is intended as a course resource for mathematically sophisticated undergraduates and students in a statistics master's program. It would serve this purpose admirably and would be a very good reference book for all researchers in this field."
R. Bharath, emeritus, Northern Michigan University for Choice Magazine